Due to the COVID-19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h + 30.0 h
Q2
Teacher(s)
Deville Yves;
Language
English
Main themes
- Problem solving by searching : formulating problems, uninformed and informed search search strategies, local search, evaluation of behavior and estimated cost, applications
- Constraint satisfaction : formulating problems as CSP, backtracking and constraint propagation, applications
- Games and adversarial search : minimax algorithm and Alpha-Beta pruning, applications
- Propositional logic : representing knowledge in PL, inference and reasoning, applications
- First-order logic : representing knowledge in FOL, inference and reasoning, forward and backward chaining, rule-based systems, applications
- Planning : languages of planning problems, search methods, planning graphs, hierarchical planning, extensions, applications
- AI, philosophy and ethics : "can machines act intelligently ?", "can machines really think ?", ethics and risks of AI, future of AI
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
|
Content
- Introduction
- Search
- Informed search
- Local search
- Adversarial search
- Constraint Satisfaction Problem
- Logical Agent
- First-order logic and Inference
- Classical Planning
- Planning in the real world
- Learning from examples
- Philosophical foundations & Present and future of AI
Teaching methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
- Problem-Based Learning
- Learning by doing
- 5 assignments (one per two weeks)
- Team of two students
- Limited teaching (1 hour / week)
- Feed-back of problems (1/2 hour )
- Discussion of current problem (1/2 hour)
Evaluation methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
- Exam : 70%
- Assignments : 30%.
Assignments must be personnal (team of 2). No collaboration between groups. No copying from Internet. Cheating = 0/20 all assignments. In case of failure of the missions the weight of this part will be more important. - Assignments may be realized only during the quadrimester of the course. It's not possible to realize the assignments during another quadrimester or for the exam session of september.
- The exam will be written, but in case of doubt on the part of the teacher as to the grade to be given to a student, the student may be questioned orally.
Online resources
Bibliography
- Stuart Russell, Peter Norvig, Artificial Intelligence : a Modern Approach, 3nd Edition, 2010, 1132 pages, Prentice Hall
- transparents en ligne
Faculty or entity
INFO
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Computer Science and Engineering
Master [120] in Computer Science
Master [120] in Data Science Engineering
Master [120] in Data Science: Information Technology
Master [120] in Biomedical Engineering
Master [60] in Computer Science